What is OEE?
OEE stands for Overall Equipment Effectiveness. Essentially, it is a single figure that signifies the utilisation of a machine. This can be at a job level, shift level, overall plant or enterprise level.
Overall Equipment Effectiveness (OEE) has been part of manufacturing KPIs for most manufacturers. However, a lot of companies do not fully understand OEE. This guide aims to shed light on OEE.
Why measure OEE?
Any new piece of machinery requires a large sum of investment. As such it is important to understand the utilisation of the machinery. A machine could run in theory 24 hours a day, seven days a week at its optimum speed. If this is the case, the maximum value is gained from the investment.
In reality, there are a number of elements that can affect this value. For instance, you may not run the machine 24/7. You, therefore, may wish only to measure OEE for when you are operating the machine and exclude planned downtimes, such as Preventative Maintenance, meetings, changeovers. The planned downtime should then be recorded as capacity availability.
Three main factors make up the OEE calculation
They are Availability, Performance, and Quality. These are expressed as a percentage and multiplied together to give you a single OEE figure – again expressed as a percentage.
The point of the final calculation is that it gives you a single figure to measure each form of wastage in the manufacturing process. The OEE number may then be tracked and compared against an internal benchmark, or a generally accepted target in similar industries. Where the actual numbers differ from the benchmark, a gap analysis must be undertaken.
Let’s take a look at each factor.
Availability
The calculation for availability is simply the actual production time out of the planned production time. The time that is lost due to downtime through e.g. machine failure, lack of input materials, lack of operator(s), will reduce the actual production time.
Availability example:
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Performance
Performance is the actually achieved run rate against the ideal run rate for the machine. Often the machine ideal or optimum run rate may be the figure published by the machine manufacturer. However, the ideal run rate may be affected by the situation of the machine, heat, cold, product running through, etc. Purists would say you still refer to the published run rate whilst others may suggest that expected performance may necessarily be degraded by the nature of the product being produced.
In a situation where the same product, with no expected variability, passes through a machine, such as a line in a bottling plant, we would expect the ideal run rate to remain constant and therefore variances may easily be identified.
However, if we consider a machine used in packaging carton manufacture, the machine performance can be degraded by the size of the input product, the number of slots and folds, or the quality of the material. In this situation, you may wish to measure the performance against the degraded expected run time of the specific product rather than, or maybe as well as, the ideal run rate.
Performance example:
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Quality
The final factor in the overall OEE calculation is quality. It is a measure of the sellable product divided by the total product manufactured (for the job, shift, day, week etc).
Quality example:
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OEE figure
OEE example:
| Availability | X | Performance | X | Quality | = | OEE |
| 90% | X | 95% | X | 95.7% | = | 81.8% |
By focusing on the three loss areas, one can improve this figure. In terms of Availability, you can look at activities that reduce unplanned downtime – this may be putting engineers on call, making sure you have critical spares, making sure that input products (raw materials) don’t run out, making sure that the operator is ‘available’.
Performance may be addressed, dependent perhaps on the machine and industry, by good maintenance routines to maintain speed, or in a degraded environment, redesign of the product if necessary to achieve the planned or ideal run speed.
Quality can be addressed, e.g. by improved maintenance routines or improved quality of raw materials.
The problem
You may think that agreeing about an OEE initiative and the measures is a complex task – and depending on your organisation it may well be. However, I would suggest that it is the smaller of the challenges you will face.
Collecting the data from operators on the performance factor is not always a reliable measure. If you decide to settle on manual data collection there is an inherent problem in that manual forms are often completed at the end of a shift and may not reflect the truth of what is happening. This is not because there is an inherent dishonesty in machine operators. The problem is that ‘remembering’ what happened in terms of set up time, runtime and downtime including the reasons is subject to the ‘witness effect’. By this I mean that you may get several witnesses to a crime but it is unlikely that they will all describe the suspect precisely and the same!
The longer the time between the event and the recording the greater the inaccuracy. Some companies may insist that the data is recorded at the end of the job or at the end of the shift. Some even at the end of the week. This method will entirely compromise the factor that contributes to the OEE figure.
It is likely that the figure will be overstated and your OEE will be higher than it genuinely is. There will also be a danger that the figures recorded will also be the ‘target’ figures. By this, I mean that if you allow 20 minutes to set-up a machine the operator may always take 20 minutes, or record 20 minutes, even if it is less. Does it matter? Of course – if you are not finding out the truth you may not be revealing the hidden capacity that may be utilised to improve the performance (and the OEE) of your investment. Some may suggest that collecting data manually as the events happen may be the answer- but the reality is that any manual method would also impair the OEE figure simply through the act of collecting it.
The answer….
Is that the recording of all the factors needs to be either automatic or as unobtrusive as possible.
This is addressed by the implementation of Shoplogix. Shoplogix measures through a ‘heartbeat’ sensor if the machine is running or not and at what speed it is running. Reasons for machine stoppages can be, in most instances, automatically recorded. In addition, the operator can assign reason codes and comments to the downtimes. This information will give ‘machine truth’, real time. This is the starting point to not only implementing OEE measurement but gaining the power to improve it.
Conclusion
OEE is a critical key performance indicator for any manufacturing organisation; improvements in OEE have a direct positive effect on the bottom line. To understand true OEE figures, a real-time data collection mechanism is a requirement. Accurate data is collected and presented real-time, providing the basis for directed corrective actions to increase OEE.
